

import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)


UpperCAmelCase_							:					Union[str, Any]		 =   r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''






class      lowerCamelCase_      (	_lowercase	):




	@add_start_docstrings(__A							)
	def __call__(     self						:     str					,      __A						:     torch.LongTensor					,      __A						:     torch.FloatTensor					,      **__A						:     Optional[Any]							):
		raise NotImplementedError("""StoppingCriteria needs to be subclassed"""							)






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     Union[str, Any]					,      __A						:     int					,      __A						:     Optional[int] = None							):
		__A :			Optional[int]		    =			max_length
		__A :			Optional[int]		    =			max_position_embeddings




	@add_start_docstrings(__A							)
	def __call__(     self						:     Union[str, Any]					,      __A						:     torch.LongTensor					,      __A						:     torch.FloatTensor					,      **__A						:     Optional[int]							):
		__A :			Optional[Any]		    =			input_ids.shape[-1]
		__A :			Union[str, Any]		    =			cur_len >= self.max_length
		if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
			logger.warning_once(
			    """This is a friendly reminder - the current text generation call will exceed the model's predefined """
			    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
			    """exceptions, performance degradation, or nothing at all."""							)
		return is_done






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     List[str]					,      __A						:     int					,      __A						:     int							):
		warnings.warn(
		    """The class `MaxNewTokensCriteria` is deprecated. """
		    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
		    """with `max_length = start_length + max_new_tokens` instead."""					,      __A					,      )
		__A :			Dict		    =			start_length
		__A :			Optional[int]		    =			max_new_tokens
		__A :			Tuple		    =			start_length + max_new_tokens




	@add_start_docstrings(__A							)
	def __call__(     self						:     Tuple					,      __A						:     torch.LongTensor					,      __A						:     torch.FloatTensor					,      **__A						:     str							):
		return input_ids.shape[-1] >= self.max_length






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     int					,      __A						:     float					,      __A						:     Optional[float] = None							):
		__A :			Optional[int]		    =			max_time
		__A :			int		    =			time.time() if initial_timestamp is None else initial_timestamp




	@add_start_docstrings(__A							)
	def __call__(     self						:     int					,      __A						:     torch.LongTensor					,      __A						:     torch.FloatTensor					,      **__A						:     Optional[int]							):
		return time.time() - self.initial_timestamp > self.max_time






class      lowerCamelCase_      (	_lowercase	):




	@add_start_docstrings(__A							)
	def __call__(     self						:     List[str]					,      __A						:     torch.LongTensor					,      __A						:     torch.FloatTensor					,      **__A						:     List[str]							):
		return any(criteria(__A					,      __A							) for criteria in self							)




	@property
	def        lowerCAmelCase_       (     self						:     int							):
		for stopping_criterium in self:
			if isinstance(__A					,      __A							):
				return stopping_criterium.max_length
			elif isinstance(__A					,      __A							):
				return stopping_criterium.max_length
		return None






def 							__SCREAMING_SNAKE_CASE     (   a__    :   StoppingCriteriaList			,a__    :   int       )					->				StoppingCriteriaList:
	__A :			int		    =			stopping_criteria.max_length
	__A :			Optional[int]		    =			deepcopy(a__       )
	if stopping_max_length is not None and stopping_max_length != max_length:
		warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter"""			,a__       )
	elif stopping_max_length is None:
		new_stopping_criteria.append(MaxLengthCriteria(max_length=a__       )       )
	return new_stopping_criteria






'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
